Amazon EMR and OpenText Analytics Database (Vertica) are two prominent data processing platforms in the data analytics category. Amazon EMR, with its cloud integration and scalability, seems to have the upper hand in distributing computing tasks, whereas Vertica excels in high-performance query processing and optimized storage.
Features: Amazon EMR is appreciated for its scalable architecture and seamless integration with various cloud services. It efficiently handles large-scale distributed computing with support for frameworks like Hive and Spark, and its cloud-based infrastructure simplifies the management of large datasets. Vertica is valued for its superior query performance and columnar storage, which improve data access speed and efficiency. It offers parallel data processing with strong OLAP functionalities and analytical tools that simplify management.
Room for Improvement: Amazon EMR users suggest enhancements in initial configuration simplicity, monitoring, automation features, and addressing slow job start times and cost challenges. Vertica users desire better workload management, expanded documentation, improved scalability for transactional operations, support for more storage formats, and more intuitive management tools.
Ease of Deployment and Customer Service: Amazon EMR is primarily deployed in the public cloud, providing flexibility but with occasional inconsistencies in customer support. Vertica offers versatile deployment options across on-premises, hybrid, and public cloud environments, but its customer service experience sometimes varies.
Pricing and ROI: Both Amazon EMR and Vertica are considered to be on the expensive side. Amazon EMR’s costs are usage-based, driven by cluster configurations, while Vertica provides a transparent licensing model based on data size, though its upfront costs and planning needs can create barriers. Both platforms allow for effective resource use to save on costs, with Amazon EMR users particularly needing to manage expenses carefully to avoid unexpected charges.
They help with billing, cost determination, IAM properties, security compliance, and deployment and migration activities.
We get all call support, screen sharing support, and immediate support, so there are no problems.
Scalability can be provisioned using the auto-scaling feature, EC2 instances, on-demand instances, and storage locations like block storage, S3, or file storage.
Regular updates, patch installations, monitoring, logging, alerting, and disaster recovery activities are crucial for maintaining stability.
The cost factor differs significantly. When you run Spark application on EKS, you run at the pod level, so you can control the compute cost. But in Amazon EMR, when you have to run one application, you have to launch the entire EC2.
There is room for improvement with respect to retries, handling the volume of data on S3 buckets, cluster provisioning, scaling, termination, security, and integration between services like S3, Glue, Lake Formation, and DynamoDB.
Cost optimization can be achieved through instance usage, cluster sharing, and auto-scaling.
Amazon EMR helps in scalability, real-time and batch processing of data, handling efficient data sources, and managing data lakes, data stores, and data marts on file systems and in S3 buckets.
Amazon EMR provides out-of-the-box solutions with Spark and Hive.
| Product | Market Share (%) |
|---|---|
| OpenText Analytics Database (Vertica) | 6.1% |
| Amazon EMR | 3.3% |
| Other | 90.6% |
| Company Size | Count |
|---|---|
| Small Business | 6 |
| Midsize Enterprise | 5 |
| Large Enterprise | 11 |
| Company Size | Count |
|---|---|
| Small Business | 29 |
| Midsize Enterprise | 23 |
| Large Enterprise | 38 |
OpenText Analytics Database Vertica is known for its fast data loading and efficient query processing, providing scalability and user-friendliness with a low cost per TB. It supports large data volumes with OLAP, clustering, and parallel ingestion capabilities.
OpenText Analytics Database Vertica is designed to handle substantial data volumes with a focus on speed and efficient storage through its columnar architecture. It offers advanced performance features like workload isolation and compression, ensuring flexibility and high availability. The database is optimized for scalable data management, supporting data scientists and analysts with real-time reporting and analytics. Its architecture is built to facilitate hybrid deployments on-premises or within cloud environments, integrating seamlessly with business intelligence tools like Tableau. However, challenges such as improved transactional capabilities, optimized delete processes, and better real-time loading need addressing.
What features define OpenText Analytics Database Vertica?OpenText Analytics Database Vertica's implementation spans industries such as finance, healthcare, and telecommunications. It serves as a central data warehouse offering scalable management, high-speed processing, and geospatial functions. Companies benefit from its capacity to integrate machine learning and operational reporting, enhancing analytical capabilities.
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